{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ddbaae40",
   "metadata": {},
   "source": [
    "## Measuring the volatility of a return series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "087bde0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "from openbb import obb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "deeeb8c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "obb.user.preferences.output_type = \"dataframe\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0699b2a",
   "metadata": {},
   "source": [
    "Fetches historical price data for the equity \"AAPL\" starting from 2020-01-01 using the \"yfinance\" provider and stores it in 'df'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e55a678b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = obb.equity.price.historical(\"AAPL\", start_date=\"2020-01-01\", provider=\"yfinance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1d13551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>split_ratio</th>\n",
       "      <th>dividend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>74.059998</td>\n",
       "      <td>75.150002</td>\n",
       "      <td>73.797501</td>\n",
       "      <td>75.087502</td>\n",
       "      <td>135480400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>74.287498</td>\n",
       "      <td>75.144997</td>\n",
       "      <td>74.125000</td>\n",
       "      <td>74.357498</td>\n",
       "      <td>146322800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>73.447502</td>\n",
       "      <td>74.989998</td>\n",
       "      <td>73.187500</td>\n",
       "      <td>74.949997</td>\n",
       "      <td>118387200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>74.959999</td>\n",
       "      <td>75.224998</td>\n",
       "      <td>74.370003</td>\n",
       "      <td>74.597504</td>\n",
       "      <td>108872000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>74.290001</td>\n",
       "      <td>76.110001</td>\n",
       "      <td>74.290001</td>\n",
       "      <td>75.797501</td>\n",
       "      <td>132079200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-10</th>\n",
       "      <td>196.899994</td>\n",
       "      <td>197.300003</td>\n",
       "      <td>192.149994</td>\n",
       "      <td>193.119995</td>\n",
       "      <td>97262100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-11</th>\n",
       "      <td>193.649994</td>\n",
       "      <td>207.160004</td>\n",
       "      <td>193.630005</td>\n",
       "      <td>207.149994</td>\n",
       "      <td>172373300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-12</th>\n",
       "      <td>207.369995</td>\n",
       "      <td>220.199997</td>\n",
       "      <td>206.899994</td>\n",
       "      <td>213.070007</td>\n",
       "      <td>198134300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-13</th>\n",
       "      <td>214.740005</td>\n",
       "      <td>216.750000</td>\n",
       "      <td>211.600006</td>\n",
       "      <td>214.240005</td>\n",
       "      <td>97862700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-14</th>\n",
       "      <td>213.850006</td>\n",
       "      <td>215.169998</td>\n",
       "      <td>211.300003</td>\n",
       "      <td>212.490005</td>\n",
       "      <td>69175600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1121 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  open        high         low       close     volume  \\\n",
       "date                                                                    \n",
       "2020-01-02   74.059998   75.150002   73.797501   75.087502  135480400   \n",
       "2020-01-03   74.287498   75.144997   74.125000   74.357498  146322800   \n",
       "2020-01-06   73.447502   74.989998   73.187500   74.949997  118387200   \n",
       "2020-01-07   74.959999   75.224998   74.370003   74.597504  108872000   \n",
       "2020-01-08   74.290001   76.110001   74.290001   75.797501  132079200   \n",
       "...                ...         ...         ...         ...        ...   \n",
       "2024-06-10  196.899994  197.300003  192.149994  193.119995   97262100   \n",
       "2024-06-11  193.649994  207.160004  193.630005  207.149994  172373300   \n",
       "2024-06-12  207.369995  220.199997  206.899994  213.070007  198134300   \n",
       "2024-06-13  214.740005  216.750000  211.600006  214.240005   97862700   \n",
       "2024-06-14  213.850006  215.169998  211.300003  212.490005   69175600   \n",
       "\n",
       "            split_ratio  dividend  \n",
       "date                               \n",
       "2020-01-02          0.0       0.0  \n",
       "2020-01-03          0.0       0.0  \n",
       "2020-01-06          0.0       0.0  \n",
       "2020-01-07          0.0       0.0  \n",
       "2020-01-08          0.0       0.0  \n",
       "...                 ...       ...  \n",
       "2024-06-10          0.0       0.0  \n",
       "2024-06-11          0.0       0.0  \n",
       "2024-06-12          0.0       0.0  \n",
       "2024-06-13          0.0       0.0  \n",
       "2024-06-14          0.0       0.0  \n",
       "\n",
       "[1121 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d380cb7a",
   "metadata": {},
   "source": [
    "Selects the 'close' column from 'df' and stores it in 'close'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d12f8916",
   "metadata": {},
   "outputs": [],
   "source": [
    "close = df[\"close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c3c3d8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-01-02     75.087502\n",
       "2020-01-03     74.357498\n",
       "2020-01-06     74.949997\n",
       "2020-01-07     74.597504\n",
       "2020-01-08     75.797501\n",
       "                 ...    \n",
       "2024-06-10    193.119995\n",
       "2024-06-11    207.149994\n",
       "2024-06-12    213.070007\n",
       "2024-06-13    214.240005\n",
       "2024-06-14    212.490005\n",
       "Name: close, Length: 1121, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(close)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a542d323",
   "metadata": {},
   "source": [
    "Calculates the percentage change of the 'close' prices and stores it in 'returns'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7403959",
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = close.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0dd71d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2020-01-02         NaN\n",
       "2020-01-03   -0.009722\n",
       "2020-01-06    0.007968\n",
       "2020-01-07   -0.004703\n",
       "2020-01-08    0.016086\n",
       "                ...   \n",
       "2024-06-10   -0.019148\n",
       "2024-06-11    0.072649\n",
       "2024-06-12    0.028578\n",
       "2024-06-13    0.005491\n",
       "2024-06-14   -0.008168\n",
       "Name: close, Length: 1121, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d083d9b8",
   "metadata": {},
   "source": [
    "Calculates the standard deviation of the percentage returns, excluding NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "148a8291",
   "metadata": {},
   "outputs": [],
   "source": [
    "std_dev = returns.dropna().std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8c255ecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020645474500698594"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(std_dev)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "732940d6",
   "metadata": {},
   "source": [
    "Annualizes the standard deviation by multiplying with the square root of 252 (trading days in a year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22395c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32773674736664327"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev * np.sqrt(252)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2d5858",
   "metadata": {},
   "source": [
    "Converts the index of 'close' to datetime format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dde3dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "close.index = pd.to_datetime(close.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4f8f45",
   "metadata": {},
   "source": [
    "Calculates the monthly percentage change, drops NaN values, calculates the standard deviation, and annualizes it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ae3c11c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.30826609119838494"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(close.pct_change(freq=\"ME\").dropna().std() * np.sqrt(12))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fbc5596",
   "metadata": {},
   "source": [
    "Calculates the quarterly percentage change, drops NaN values, calculates the standard deviation, and annualizes it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f460ccfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38461527636615106"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(close.pct_change(freq=\"QE\").dropna().std() * np.sqrt(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e158e0",
   "metadata": {},
   "source": [
    "Calculates the rolling 22-day standard deviation of the daily percentage changes, annualizes it, and plots the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce7e7e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(close.pct_change().rolling(window=22).std() * np.sqrt(252)).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18af874a",
   "metadata": {},
   "source": [
    "**Jason Strimpel** is the founder of <a href='https://pyquantnews.com/'>PyQuant News</a> and co-founder of <a href='https://www.tradeblotter.io/'>Trade Blotter</a>. His career in algorithmic trading spans 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology. His career spans America, Europe, and Asia. He shares his expertise through the <a href='https://pyquantnews.com/subscribe-to-the-pyquant-newsletter/'>PyQuant Newsletter</a>, social media, and has taught over 1,000+ algorithmic trading with Python in his popular course **<a href='https://gettingstartedwithpythonforquantfinance.com/'>Getting Started With Python for Quant Finance</a>**. All code is for educational purposes only. Nothing provided here is financial advise. Use at your own risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1797f265-ae8b-44f6-bfdc-82963ce8345e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
